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Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX.
Pan, Huaqin; Bakalov, Vesselina; Cox, Lisa; Engle, Michelle L; Erickson, Stephen W; Feolo, Michael; Guo, Yuelong; Huggins, Wayne; Hwang, Stephen; Kimura, Masato; Krzyzanowski, Michelle; Levy, Josh; Phillips, Michael; Qin, Ying; Williams, David; Ramos, Erin M; Hamilton, Carol M.
Affiliation
  • Pan H; RTI International, Research Triangle Park, NC, USA. hpan@rti.org.
  • Bakalov V; RTI International, Research Triangle Park, NC, USA.
  • Cox L; RTI International, Research Triangle Park, NC, USA.
  • Engle ML; RTI International, Research Triangle Park, NC, USA.
  • Erickson SW; RTI International, Research Triangle Park, NC, USA.
  • Feolo M; National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
  • Guo Y; GeneCentric Therapeutics Inc., Durham, NC, USA.
  • Huggins W; RTI International, Research Triangle Park, NC, USA.
  • Hwang S; RTI International, Research Triangle Park, NC, USA.
  • Kimura M; National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
  • Krzyzanowski M; RTI International, Research Triangle Park, NC, USA.
  • Levy J; Levy Informatics, Chapel Hill, NC, USA.
  • Phillips M; RTI International, Research Triangle Park, NC, USA.
  • Qin Y; RTI International, Research Triangle Park, NC, USA.
  • Williams D; RTI International, Research Triangle Park, NC, USA.
  • Ramos EM; National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
  • Hamilton CM; RTI International, Research Triangle Park, NC, USA.
Sci Data ; 9(1): 532, 2022 09 01.
Article in En | MEDLINE | ID: mdl-36050327
Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Data Collection / Datasets as Topic Type of study: Observational_studies / Prognostic_studies Language: En Journal: Sci Data Year: 2022 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Data Collection / Datasets as Topic Type of study: Observational_studies / Prognostic_studies Language: En Journal: Sci Data Year: 2022 Document type: Article Affiliation country: Country of publication: